Abstract
The ECG has considerable diagnostic significance in medicine. It is important to detect and display waveforms on the ECG recordings fast and automatically. In this study, waveform detection is performed by using artificial neural networks (ANN's). After the detection of the R peak of the QRS complex, feature vectors are formed by using the amplitudes of the significant frequency components of the DFT frequency spectrum. Grow and Learn (GAL) and Kohonen networks are comparatively examined to detect 4 different ECG waveforms. The comparative performance results of GAL and Kohonen networks indicate that the GAL network results in faster learning and better classification performance with less number of nodes.
| Original language | English |
|---|---|
| Pages (from-to) | 929-930 |
| Number of pages | 2 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 3 |
| Publication status | Published - 1996 |
| Event | Proceedings of the 1996 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 5) - Amsterdam, Neth Duration: 31 Oct 1996 → 3 Nov 1996 |